论文标题
部分可观测时空混沌系统的无模型预测
Mitigating Shadows in Lidar Scan Matching using Spherical Voxels
论文作者
论文摘要
在本文中，我们提出了一种通过基于球形网格的预处理步骤来减轻激光扫描匹配中阴影错误的方法。由于网格与激光束对齐，因此消除阴影边缘相对容易，从而导致LiDAR扫描匹配的系统错误。正如我们通过仿真所示，我们提出的算法比地面平面删除提供了更好的结果，而平面删除是降低阴影的最常见策略。与拆除地面平面不同，我们的方法适用于任意地形（例如，城市墙壁上的阴影，丘陵地形的阴影），同时保留地面上的密钥发光点，这对于估计高度，俯仰和滚动的变化至关重要。我们的预处理算法可以与一系列扫描匹配方法一起使用。但是，对于基于体素的扫描匹配方法，它通过降低计算成本和在体素之间更均匀分配激光点来提供额外的好处。
In this paper we propose an approach to mitigate shadowing errors in Lidar scan matching, by introducing a preprocessing step based on spherical gridding. Because the grid aligns with the Lidar beam, it is relatively easy to eliminate shadow edges which cause systematic errors in Lidar scan matching. As we show through simulation, our proposed algorithm provides better results than ground-plane removal, the most common existing strategy for shadow mitigation. Unlike ground plane removal, our method applies to arbitrary terrains (e.g. shadows on urban walls, shadows in hilly terrain) while retaining key Lidar points on the ground that are critical for estimating changes in height, pitch, and roll. Our preprocessing algorithm can be used with a range of scan-matching methods; however, for voxel-based scan matching methods, it provides additional benefits by reducing computation costs and more evenly distributing Lidar points among voxels.
